zixuanweeei commented on issue #14713: MKLDNN RNN Inference Integration(fp32 LSTM and vRNN with tanh and relu) URL: https://github.com/apache/incubator-mxnet/pull/14713#issuecomment-495270739 We also test the performances over GPU of our PR and the master. Here is the the result. The relative DIFF is calculated by (Our_PR - MASTER) / MASTER. In summary, Our modifications have no significant damage to the performance over GPU. ### Layer = 1, bidirectional = False | API | Our PR (sample/sec) | MASTER (sample/sec) | DIFF (sample/sec) | Relative DIFF | |-----|---------------------|---------------------|-------------------|---------------| |FusedLSTM |1038 |1058 |-20 |-1.89%| |FusedvRNN with tanh |1961 |1884 |77 |4.09% |FusedvRNN with relu |1926 |1939 |-13 |-0.67%| ### Layer = 1, bidirectional = True | API | Our PR (sample/sec) | MASTER (sample/sec) | DIFF (sample/sec) | Relative DIFF | |-----|---------------------|---------------------|-------------------|---------------| |FusedLSTM |683 |694 |-11 |-1.59%| |FusedvRNN with tanh |1221 |1190 |31 |2.61%| |FusedvRNN with relu |1212 |1209 |3 |0.25%| ### Layer = 5, bidirectional = False | API | Our PR (sample/sec) | MASTER (sample/sec) | DIFF (sample/sec) | Relative DIFF | |-----|---------------------|---------------------|-------------------|---------------| |FusedLSTM |322 |320 |2 |0.63%| |FusedvRNN with tanh |676 |649 |27 |4.16%| |FusedvRNN with relu |670 |637 |33 |5.18%| ### Layer = 5, bidirectional = True | API | Our PR (sample/sec) | MASTER (sample/sec) | DIFF (sample/sec) | Relative DIFF | |-----|---------------------|---------------------|-------------------|---------------| |FusedLSTM |110 |109 |1 |0.92%| |FusedvRNN with tanh |210 |209 |1 |0.48%| |FusedvRNN with relu |212 |210 |2 |0.95%|
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